class_docstr: Crops a random spatial crop in a spatio-temporal
  numpy or tensor input [Channel, Time, Height, Width]
docstr: Args:
      size (tuple): in format (height, width)
docstr_tok: ["Args",":","size","(","tuple",")",":","in","format","(","height",",","width",")"]
code: def __init__(self, size):
      """
      Args:
          size (tuple): in format (height, width)
      """
      self.size = size
  
file_path: gluon-cv/gluoncv/torch/data/transforms/videotransforms/tensor_transforms.py
imports: import random
  from .utils import functional
label_desc: Use image node_id 1 to create a new SpatialRandomCrop object from inherited base classes: object with example: obj = SpatialRandomCrop(size)